Author: Victor Navarro

# HD2022

#### Victor Navarro

# The mathematics behind HeiDI

The HeiDI model has four major components: 1) the acquisition of reciprocal associations between stimuli, 2) the pooling of those associations into stimulus activations, 3) the distribution of those activations into stimulus-specific response units, and 4) the generation of responses.

## 1 - Acquiring reciprocal associations

Whenever a trial is given, HeiDI learns associations among stimuli. The association between two stimuli, \(i\) and \(j\) is denoted via \(v_{i,j}\). The association \(v_{i,j}\) represents a directional expectation: the expectation of \(j\) after being presented with \(i\). Furthermore, its value represents the nature of the effect that \(i\) has over the representation of \(j\). If positive, the presentation of \(i\) “excites” the representation of \(j\). If negative, the presentation of \(i\) “inhibits” the representation of \(j\).

HeiDI not only learns “forward” associations between stimuli, but also their reciprocal, or “backward” associations. Thus, if organisms are presented with \(i \rightarrow j\), organisms not only learn about \(v_{i,j}\), but also about \(v_{j, i}\), or the expectation of receiving \(i\) after being presented with \(j\). Note that, for the sake of brevity, the learning equations below are only specified for forward associations.

### 1.1 - The stimulus expectation rule

HeiDI generates expectations about stimuli. The expectation of stimulus \(j\) (\(e_j\)) is expressed as

\[ \tag{Eq. 1} e_j = \sum_{k}^{K}x_kv_{k,j} \]

where \(K\) is the set containing all stimuli in the experiment, and \(x_k\) is a quantity denoting the presence or absence of stimulus \(k\) (1 or 0, respectively)1.

### 1.2 - Learning rule

HeiDI learns the appropriate expectations via error-correction mechanisms. After trial \(t\), the association between stimuli \(i\) and \(j\) is expressed as

\[ \tag{Eq. 2} v_{i,j, t} = v_{i,j, t-1} + \Delta v_{i,j, t} \]

where \(v_{j,i, t-1}\) is the forward association between \(i\) and \(j\) on trial \(t-1\), and \(\Delta v_{i,j, t}\) is the change in that association as a result of trial \(t\). That delta term uses a pooled error term and is expressed as

\[ \tag{Eq. 3} \Delta v_{i,j} = x_i\alpha_i(x_jc\alpha_j - e_j) \] where \(\alpha_i\) and \(\alpha_j\) are parameters representing the salience of stimuli \(i\) and \(j\), respectively (\(0 \le \alpha \le 1\)), \(c\) is a scaling constant (\(c = 1\)). Note that the term denoting the trial, \(t\) has been omitted here for simplicity.

## 2 - Pooling the strength of associations

HeiDI pools its stimulus associations to activate stimulus-specific representations. The activation of the representation for stimulus \(j\), \(a_j\), is defined as:

\[ \tag{Eq. 4} a_{j,M} = o_{j,M} + h_{j,M} \]

where \(o_{j,M}\) denotes the combined associative strength towards stimulus \(j\) in presence of stimuli \(M\), and \(h_{j,M}\) denotes the chained associative strength towards stimulus \(j\) in presence of stimuli \(M\).

### 2.1 - Combined associative strength

The quantity \(o_{j,M}\) is the result of combining the associative strength of forward and backward associations to and from stimulus \(j\) as

\[ \tag{Eq. 5} o_{j,M} = \sum_{m \neq j}^{M}v_{m,j} + \left(\frac{\sum_{m \neq j}^{M}v_{m,j} \sum_{m \neq j}^{M}v_{j,m}}{c}\right) \]

where each of the sums above run over all stimuli \(M\) presented in the trial, different from stimulus \(j\).2 The left-hand term describes how the forward associations from stimuli \(M\) to \(j\) affect the representation of \(j\), whereas the right-hand term describes how the backward associations that \(j\) has with stimuli \(M\) affect its representation (although these are modulated by the forward associations themselves).

### 2.2 - Chained associative strength

The quantity \(h_{j,M}\) captures the indirect associative strength that the stimuli \(M\) have with \(j\), via absent stimuli. As such, \(h_{j,M}\) is defined as

\[ \tag{Eq. 6a} h_{j,M} = \sum_{m \neq j}^{M} \sum_{n}^{N}\frac{v_{m,n}o_{j,n}}{c} \]

where N are the stimuli not presented on the trial (i.e., K-M). Note the re-use of \(o\), the quantity defined in Eq. 5. This equation allows absent stimuli \(N\) to influence the representation of stimulus \(j\), as long as they have an association with present stimuli \(M\).

In Honey and Dwyer (2022), the authors specify a similarity-based mechanism that modulates the effect of associative chains according to the similarity of the salience of nominal and retrieved stimuli3. As such, Eq. 6a is expanded as:

\[ \tag{Eq. 6b} h_{j,M} = \sum_{m \neq j}^{M} \sum_{n}^{N}S(\alpha_{n}, \alpha'_n)\frac{v_{m,n}o_{j,n}}{c} \]

where \(S\) is a similarity function that takes the nominal salience of stimulus n, \(\alpha_n\) (as perceived when \(n\) is presented on a trial) and its retrieved salience, \(\alpha'_n\) (as perceived when \(n\) is retrieved via other stimuli M, see ahead). This function is defined as:

\[ \tag{Eq. 7} S(\alpha_n, \alpha'_n) = \frac{\alpha_n}{\alpha_n + |\alpha_n-\alpha'_n|} \times \frac{\alpha'_n}{\alpha'_n+ |\alpha_n-\alpha'_n|} \]

Notably, whenever there is more than one nominal salience for a given stimulus, then \(\alpha_n\) is the arithmetic mean among all nominal values (see “heidi_similarity” vignette).

## 3 - Distributing strength into stimulus-specific response units

HeiDI then distributes the pooled stimulus-specific strength among all \(K\) stimuli, according to their relative salience. The activation of response unit \(j\), \(R_j\) is expressed as

\[ \tag{Eq. 8} R_{j,k} = \frac{\theta(j)}{\sum_{k}^{K}\theta(k)}a_{k,M} \]

where \(j \in K\). As \(K\) can include both present and absent stimuli, the \(\theta\) function above depends on whether the stimulus \(k\) is absent (i.e., \(k \in N\)) or not (i.e., \(k \in M\)), as:

\[ \tag{Eq. 9} \theta(k) = \begin{cases} \left |\sum_{m}^{M}\left( v_{m,k}+\sum_{n \neq k}^{N}\frac{v_{m,n}v_{n,k}}{c}\right) \right|,& \text{if } k \in N\\ \alpha_k, & \text{otherwise} \end{cases} \]

Note that the quantity for absent stimuli is absolute, to prevent negative \(\theta\) values due to inhibitory associations4. Also, note a summation term is used on the left-hand side of the expression for an absent stimulus. It implies that all the present stimuli \(M\) contribute to the salience of stimulus \(k\). Finally, note on the right-hand side of the same expression that the present stimuli contribute not only via the direct association each of them has with \(k\), \(v_{m,k}\) but also through associative chains with other absent stimuli (c.f., Eq. 6a).

## 4 - Generating responses

Finally, HeiDI responds. The response-generating mechanisms in HeiDI are currently underspecified. In its current version, HeiDI’s responses are the product of the activation of stimulus-specific response units and the connection that those units have with specific motor units. As such, the activation of motor unit \(q\), \(r_q\), is given by

\[ \tag{Eq. 10} r_q = R_jw_{j,q} \]

where \(w_{j,q}\) is a weight representing the association between stimulus-specific unit \(j\) and motor unit \(q\).

1. We go the extra length of specifying \(x\) quantities because the stimulus expectation and learning rules can be vectorized, as \(\textbf{e} = \textbf{x}V\) and \(\Delta V = (\textbf{x}\odot\textbf{a})' (c(\textbf{x}\odot\textbf{a})-\textbf{e})\), respectively. Here, the matrix \(V\) contains all associations between each pair of stimuli, the row vectors \(\textbf x\) and \(\textbf a\) denote the presence and salience of all stimuli \(K\), the \(\odot\) symbol specifies element-wise multiplication, and the \('\) symbol denotes transposition. Note further that the \(\Delta V\) matrix must be made hollow before summing it to \(V\).↩︎

2. An alternative formulation of this equation could be \(\sum_{m \neq j}^{M} v_{m,j} + (v_{m,j} v_{j,m})\) but, although this alternative formulation is positively related to Eq. 5, we have not compared their behavior exhaustively.↩︎

3. This mechanism is in model `HD2022` but not in model `HDI2020`↩︎

4. An alternative and perhaps more naturalistic parametrization of this rule would be to use \(min[0,\theta(n)]\), where \(min\) is the minimum function and \(n\) is an absent stimulus; ReLUs are extensively used in neural networks. Another alternative that avoids the use of absolute values or a rectifying mechanism would be to use quantities of \(e^{\theta(k)}\) instead of \(\theta(k)\).↩︎